DOE OSTI · 3407552
Toward an event-level analysis of hadron structure using differential programming
Abstract
Reconstructing the internal properties of hadrons in terms of fundamental quark and gluon de- grees of freedom is a central goal in nuclear and particle physics. This effort lies at the core of major experimental programs, such as the Jefferson Lab 12 GeV program and the upcoming Electron-Ion Collider. A primary challenge is the inherent inverse problem: converting large-scale observational data from collision events into the fundamental QCD-defined densities that characterize the micro- scopic structure of hadronic systems. Recent advances in AI and machine learning have opened new avenues for addressing this challenge using deep learning techniques. A particularly promising direction is the integration of complex theoretical calculations and experimental simulations into a unified framework capable of reconstructing these densities directly from event-level information. In this document, we introduce a key algorithm called LOITS, which enables differentiable program- ming within such a framework, facilitating the use of AI/ML techniques to solve the inverse problem of QCF reconstruction at the event level.
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Braga, Kevin [College of William and Mary, Williamsburg, VA (United States)] (ORCID:0009000887528292), Diefenthaler, Markus [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)] (ORCID:0000000247174484), Goldenberg, Steven [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)] (ORCID:0000000252646298), Lersch, Daniel [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)], Li, Yaohang [Old Dominion Univ., Norfolk, VA (United States)] (ORCID:0000000301781876), Qiu, Jian-Wei [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)], Rajput, Kishansingh [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States); Univ. of Houston, TX (United States)], Ringer, Felix [Stony Brook Univ., NY (United States)] (ORCID:0000000259393510), Sato, Nobuo [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)] (ORCID:0000000215356208), Schram, Malachi [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)] (ORCID:0000000234752871). 2026-09-01. Toward an event-level analysis of hadron structure using differential programming. https://doi.org/10.1016/j.physletb.2026.140816
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